Fault diagnosis of gearbox based on wavelet packet transform and CLSPSO-BP - Xiao, Maohua; Zhang, Wei; Zhao, Yuanfang; Xu, Xiaomei; Zhou, Shufang (Multimedia tools and applications, 2022)
Fault diagnosis of gearbox is difficult due to the complexity and instability of its vibration signal. A fault diagnosis method of gearbox based on WPT-CLSPSO-BP (Wavelet Packet Transform- Chaos Particle Swarm Optimization-Back Propagation Neural Network) is proposed in this study to solve this problem. Wavelet packet transform (WPT) is used to decompose and reconstruct the signal, and the energy value of each component is calculated according to the formula, and the energy value is used as a feature input to form a feature sample. Aiming at the problem of slow convergence speed and easy local optimization of traditional BP neural network, a chaotic particle swarm algorithm is proposed to optimize the weight and threshold of the network, and the optimized network performance is verified with the data collected by experiments. Experimental results show that the average diagnosis rate of CLSPSO-BP is above 92%,the trained model not only has a high diagnostic rate, which can be improved by nearly 10%, but also can keep the error value between the actual output and the predicted output below 0.1%, indicating that the optimized network has a higher fault recognition rate.